Bruijn Sjoerd M, van Dieën Jaap H, Meijer Onno G, Beek Peter J
Research Institute MOVE, Faculty of Human Movement Sciences, VU University Amsterdam, Amsterdam, The Netherlands.
J Neurosci Methods. 2009 Apr 15;178(2):327-33. doi: 10.1016/j.jneumeth.2008.12.015. Epub 2008 Dec 24.
Recently, two methods for quantifying a system's dynamic stability have been applied to human locomotion: local stability (quantified by finite time maximum Lyapunov exponents, lambda(S-stride) and lambda(L-stride)) and orbital stability (quantified as maximum Floquet multipliers, MaxFm). Thus far, however, it has remained unclear how many data points are required to obtain precise estimates of these measures during walking, and to what extent these estimates are sensitive to changes in walking behaviour. To resolve these issues, we collected long data series of healthy subjects (n=9) walking on a treadmill in three conditions (normal walking at 0.83 m/s (3 km/h) and 1.38 m/s (5 km/h), and walking at 1.38 m/s (5 km/h) while performing a Stroop dual task). Data series from 0.83 and 1.38 m/s trials were submitted to a bootstrap procedure and paired t-tests for samples of different data series lengths were performed between 0.83 and 1.38 m/s and between 1.38 m/s with and without Stroop task. Longer data series led to more precise estimates for lambda(S-stride), lambda(L-stride), and MaxFm. All variables showed an effect of data series length. Thus, when estimating and comparing these variables across conditions, data series covering an equal number of strides should be analysed. lambda(S-stride), lambda(L-stride), and MaxFm were sensitive to the change in walking speed while only lambda(S-stride) and MaxFm were sensitive enough to capture the modulations of walking induced by the Stroop task. Still, these modulations could only be detected when using a substantial number of strides (>150).
最近,两种量化系统动态稳定性的方法已应用于人类运动:局部稳定性(通过有限时间最大李雅普诺夫指数λ(S步幅)和λ(L步幅)量化)和轨道稳定性(量化为最大弗洛凯乘数,MaxFm)。然而,到目前为止,尚不清楚在步行过程中需要多少数据点才能获得这些测量值的精确估计,以及这些估计对步行行为变化的敏感程度。为了解决这些问题,我们收集了9名健康受试者在三种条件下在跑步机上行走的长数据系列:以0.83米/秒(3公里/小时)和1.38米/秒(5公里/小时)的速度正常行走,以及在执行斯特鲁普双重任务时以1.38米/秒(5公里/小时)的速度行走。将来自0.83米/秒和1.38米/秒试验的数据系列提交给自举程序,并对不同数据系列长度的样本进行配对t检验,分别在0.83米/秒和1.38米/秒之间以及在有和没有斯特鲁普任务的1.38米/秒之间进行。更长的数据系列导致对λ(S步幅)、λ(L步幅)和MaxFm的估计更精确。所有变量均显示出数据系列长度的影响。因此,在跨条件估计和比较这些变量时,应分析覆盖相同步数的数据系列。λ(S步幅)、λ(L步幅)和MaxFm对步行速度的变化敏感,而只有λ(S步幅)和MaxFm足够敏感以捕捉斯特鲁普任务引起的步行调制。不过,只有在使用大量步幅(>150)时才能检测到这些调制。